TAMU ECMT 463 Final

Term
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causality only makes sense in terms of ___
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Terms in this set (28)
causality only makes sense in terms of ___
conterfactual
T causes Y only makes sense relative to what?
What would have happened in the absence of T
Causal Effect
Yi(1) - Yi(0)
Missing Counterfactual
When both are never observed simultaneously for the same individual
The outcome that would have been for those who chose treatment if they had not been treated
E(Y(0)|T=1)
Missing Counterfactual or the unobservable
Randomization
Avoids systematic differences between treatment groups
Natural Experiment
Relies on an exogenous shock from nature
hard to replicate
requires extensive data
Panel Data
studies changes in the dependent variable over time
3 people, 3 times, 3 observations
Unbalanced panel causes problem if
A.Observations are missing randomly
B. Missing data for some i is correlated with the idiosyncratic errors Uit
C. We have a binary dependent variable
D. It never causes any problem
Missing data for some i is correlated with the idiosyncratic errors Uit
Fixed effects for each entity can control for
A. Variables that are constant over time but vary across entities
B. Variables that are constant across entities but varies across time
C. Variables that varies across time and entities
D. Variables that are constant across time and entities
Variables that are constant over time but vary across entities
If the regression errors are correlated over time within an entity A. It introduces attenuation bias B. It causes omitted variable bias C. It causes multicollinearity D. It effects varianceIt effects varianceWe do the following if we need the standard errors to allow for autocorrelation and heteroskedasicity A. We use time fixed effects B. We use country fixed effects C. We use a balanced panel data D. We use robust standard errorsWe use robust standard errorsThe rule of thumb for checking for weak instruments is as follows: for the case of a single endogenous regressor, A. a first stage F must be statistically significant to indicate a strong instrument B. a first stage F>1.96 indicates that the instruments are weak C. the t-statistic on each of the instruments must exceed at least 1.64 D. a first stage F<10 indicates that the instruments are weaka first stage F<10 indicates that the instruments are weakThe distinction between endogenous and exogenous variables is A. whether or not the variables are correlated with the error term B. dependent on the sample size: for n>100, endogenous variables become exogenous C. depends on the distribution of the variables: when they are normally distributed, they are exogenous, otherwise they are endogenous D. that exogenous variables are determined inside the model and endogenous variables are determined outside the modelwhether or not the variables are correlated with the error termThe two conditions for a valid instrument are A. corr(Zi,Xi)=0 and corr(Zi,ui) =/0 B. corr(Zi,Xi)=/0 and corr(Zi,ui) =0 C. corr(Zi,Xi)=0 and corr(Zi,ui) =0 D. corr(Zi,Xi)=/0 and corr(Zi,ui) =/0corr(Zi,Xi)=/0 and corr(Zi,ui) =0Instrument relevance A. means that the instrument is one of the determinants of dependent variables B. means that some of the variance in the regressor is related to variation in the instrument C. is the same as instrument exogeneity D. is not possible since X and u are correlated and Z and u are not correlatedmeans that some of the variance in the regressor is related to variation in the instrumentThe following will not cause correlation between X and u in the simple regression model: A. irrelevance of the regressor B. simultaneous causality C. omitted variables D. errors in variablesirrelevance of the regressorThe following are examples of limited dependent variables, with the exception of A. binary dependent variable B. truncated data C. log-log specification D. discrete choicelog-log specificationThe binary dependent variable model is an example of a A. model that cannot be estimated by OLS B. regression model, which has as a regressor, among others, a binary variable C. model where the left-hand variable is measured in base 2 D. limited dependent variable modellimited dependent variable modelIn the binary dependent variable model, a predicted value of 0.7 means that A. the most likely value the dependent variable will take on is 70 percent B. given the values for the explanatory variables, there is a 70 percent probability that the dependent variable will equal one C. given the values for the explanatory variables, there is a 30 percent probability that the dependent variable will equal one D. the model makes little sense, since the dependent variable can only be 0 or 1given the values for the explanatory variables, there is a 70 percent probability that the dependent variable will equal oneThe linear probability model is A. the application of the linear multiple regression model (OLS) to a binary dependent variable B. an example of probit estimation C. another word for logit estimation D. the application of the multiple regression model with a continuous left-hand side variable and a binary variable as at least one of the regressorsthe application of the linear multiple regression model (OLS) to a binary dependent variableIn the linear probability model, the interpretation of the slope coefficient is A. the change in odds associated with a unit change in X, holding other regressors constant B. not all that meaningful since the dependent variable is either 0 or 1 C. the change in probability that Y=1 associated with a unit change in X, holding others regressors constant D. the response in the dependent variable to a percentage change in the regressorthe change in probability that Y=1 associated with a unit change in X, holding others regressors constantThe following tools from multiple regression analysis carry over in a meaningful manner to the linear probability model, with the exception of the A. regression 𝑅^2 B. significance test using the t-statistic C. 95% confidence interval using ± 1.96 times the standard error D. F-statisticregression 𝑅^2The major flaw of the linear probability model is that A. the regression 𝑅^2 cannot be used as a measure of fit B. the predicted values can lie above 1 or below 0 C. people do not always make clear-cut decisions D. the actuals can only be 0 and 1, but the predicted are almost always different from thatthe predicted values can lie above 1 or below 0The probit model A. is identical to the linear regression model B. always gives the same fit for the predicted values as the linear probability model for values between 0.1 and 0.9 C. forces the predicted values to lie between 0 and 1 D. should not be used since it is too complicatedforces the predicted values to lie between 0 and 1In the expression 𝑃(𝑑𝑒𝑛𝑦 = 1 𝑃/𝐼 𝑅𝑎𝑡𝑖𝑜, 𝑏𝑙𝑎𝑐𝑘) = 𝛷(- 2.26 + 1.58𝑃/𝐼 𝑟𝑎𝑡𝑖𝑜 + 0.71𝑏𝑙𝑎𝑐𝑘), the effect of increasing the P/I ratio from 0.3 to 0.4 for a white person A. should not be interpreted without knowledge of the regression 𝑅^2 B. is 0.158 percentage points C. is 1.58 percentage points D. cannot be calculated without more informationcannot be calculated without more informationA friend of yours is studying the probability of engaging in new criminal activity for defendants who are awaiting trial for another prior offense. This is known as recidivism. For this purpose, your friend limits the sample to defendants arrested on Friday and Saturday nights only. Your friend's sample most likely suffers from A. Truncated observations B. Sample selection C. Censored observations D. None of the aboveSample selectionFixed effect regressionmethod for controlling for omitted variables in panel data when the omitted variables vary across entities but don't change over time